Cross-Device Tracking: Matching Devices and Cookies

The number of computers, tablets and smartphones is increasing rapidly, which entails the ownership and use of multiple devices to perform online tasks. As people move across devices to complete these tasks, their identities becomes fragmented. Understanding the usage and transition between those devices is essential to develop efficient applications in a multi-device world. In this paper we present a solution to deal with the cross-device identification of users based on semi-supervised machine learning methods to identify which cookies belong to an individual using a device. The method proposed in this paper scored third in the ICDM 2015 Drawbridge Cross-Device Connections challenge proving its good performance

Perceptions of Electoral Fairness and Voter Turnout

Previous research has established a link between turnout and the extent to which voters are faced with a “meaningful” partisan choice in elections; this study extends the logic of this argument to perceptions of the “meaningfulness” of electoral conduct. It hypothesizes that perceptions of electoral integrity are positively related to turnout. The empirical analysis to test this hypothesis is based on aggregate-level data from 31 countries, combined with survey results from Module 1 of the Comparative Study of Electoral Systems survey project, which includes new and established democracies. Multilevel modeling is employed to control for a variety of individual- and election-level variables that have been found in previous research to influence turnout. The results of the analysis show that perceptions of electoral integrity are indeed positively associated with propensity to vote. </jats:p

Entropy/IP: Uncovering Structure in IPv6 Addresses

In this paper, we introduce Entropy/IP: a system that discovers Internet address structure based on analyses of a subset of IPv6 addresses known to be active, i.e., training data, gleaned by readily available passive and active means. The system is completely automated and employs a combination of information-theoretic and machine learning techniques to probabilistically model IPv6 addresses. We present results showing that our system is effective in exposing structural characteristics of portions of the IPv6 Internet address space populated by active client, service, and router addresses. In addition to visualizing the address structure for exploration, the system uses its models to generate candidate target addresses for scanning. For each of 15 evaluated datasets, we train on 1K addresses and generate 1M candidates for scanning. We achieve some success in 14 datasets, finding up to 40% of the generated addresses to be active. In 11 of these datasets, we find active network identifiers (e.g., /64 prefixes or `subnets') not seen in training. Thus, we provide the first evidence that it is practical to discover subnets and hosts by scanning probabilistically selected areas of the IPv6 address space not known to contain active hosts a priori.Comment: Paper presented at the ACM IMC 2016 in Santa Monica, USA (https://dl.acm.org/citation.cfm?id=2987445). Live Demo site available at http://www.entropy-ip.com